Planning is the process of finding a course of action that can be executed to achieve a goal. Artificial Intelligence (AI) techniques have been applied to solve real-world planning problems by assisting in smart process management, i.e. the production of a plan of action (or process) for accomplishing a mission, based upon input knowledge including knowledge of the current state of associated real-world entities. Such a plan of action (also referred to as a mission plan) typically includes one or more sequential and/or parallel sequences of actions (or mission steps) specified to be performed at particular times, such that by following the plan of action a desired end state can be approached (e.g., the mission can be at least partly accomplished).
Smart Process Management techniques (otherwise known as Artificial Intelligence Planning and Scheduling techniques) are an example of AI techniques for the solution of planning problems. A Smart Process Management technique typically involves receiving one or more mission goals and input knowledge regarding the current state of one or more resources and, using that input knowledge, the Smart Process Management technique produces a plan of action for carrying out the mission goals.
Hierarchical Task Network (HTN) Smart Process Management techniques are acknowledged as one of the most efficient of such AI techniques. However, problems exist with HTN Smart Process Management techniques because the HTN Smart Process Management techniques do not work satisfactorily when the input knowledge is imperfect or incompletely known (e.g., in so-called “real-world” situations, when the input knowledge is subject to or includes uncertainty, vagueness, lack of precision, and/or incompleteness, and actions are not completely deterministic (i.e., the result of carrying out an action cannot be completely predicted)). So-called “classical” planning techniques typically make simplifying assumptions, one of which is to consider that initial state parameters are fully determined, and another of which is to consider that after performing an action the resulting state can be predicted with complete certainty. These assumptions do not apply well to so-called “real-world” planning problems, and thus planning techniques (e.g., theory and algorithms) for planning in such “real-world” situations are more complex than classical techniques.
Although a few existing HTN techniques are able to cope somewhat with imperfect knowledge, all of those few are focused on reactive problems such as robotics, and none of them have been effective in relation to deliberative processes such as Smart Process Management. Non-HTN techniques are generally less efficient than HTN techniques and have only very limited capabilities when dealing with real-world problems. Thus, the availability of efficient AI techniques for Smart Process Management of real-world problems is currently limited.